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August 24, 2025Open Access

Comparison of Foundation and Supervised Learning-Based Models for Detection of Referable Glaucoma from Fundus Photographs

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The cropped image VGG-19 model achieved the highest AUC-ROC of 0.924, indicating superior classification performance.
Cropped image models consistently outperformed uncropped models across architectures, highlighting the importance of image selection.
Assessment using diagnostic technology involved 6,116 participants from a teleretinal screening program to validate model efficacy.
Foundation models may perform better with smaller datasets, suggesting strategic choices in model training setup.

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Implication

Evaluation compares performance of foundation and supervised learning models for glaucoma detection from fundus photos, suggesting crop images improve outcomes.

Cite This Study

Bolo et al. (2025) studied this question.

synapsesocial.com/papers/68af5bc7ad7bf08b1eae025f https://doi.org/10.1101/2025.08.21.25334170

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Implication

Evaluation compares performance of foundation and supervised learning models for glaucoma detection from fundus photos, suggesting crop images improve outcomes.

Comparison of Foundation and Supervised Learning-Based Models for Detection of Referable Glaucoma from Fundus Photographs

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Discussion

Implication

Cite This Study

Comparison of Foundation and Supervised Learning-Based Models for Detection of Referable Glaucoma from Fundus Photographs

Key Points

Discussion

Implication

Cite This Study

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